CIMMYT Research Data

Free, open access repository of research studies developed by CIMMYT scientists.

Featured Dataverses

In order to use this feature you must have at least one published or linked dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

7,131 to 7,140 of 10,508 Results

06CHTTEW-Grain Yield.xls Aug 11, 2019 - International Early White Hybrid Trial - IEWH0611 MS Excel Spreadsheet - 29.0 KB - MD5: f97895ea65da34f9ebde0ed06de7560f Combined Analysis
06CHTTEW-Locations.xls Aug 11, 2019 - International Early White Hybrid Trial - IEWH0611 MS Excel Spreadsheet - 28.5 KB - MD5: 7b8fcaaad89932368b3922c72118c17c Locations
Long-term tillage and residue management experiment in San Luis Potosí, Mexico Aug 4, 2019 Verhulst, Nele; Fonteyne, Simon; Martinez Gamiño, Miguel-Angel; Saldivia Tejeda, Abel, 2019, "Long-term tillage and residue management experiment in San Luis Potosí, Mexico", https://hdl.handle.net/11529/10548248, CIMMYT Research Data & Software Repository Network, V1, UNF:6:IoKHUBaC8wFwyZ40ygz99g== [fileUNF] An experiment initiated in 1996 in the highlands of the state of San Luis Potosí, Mexico, evaluated different tillage methods and levels of soil cover under permanent raised beds for their effects on yield, profitability, and soil quality in an irrigated, summer maize- winter oat...
DAT-PUB-Soledad.tab Aug 4, 2019 - Long-term tillage and residue management experiment in San Luis Potosí, Mexico Tabular Data - 3.8 KB - 5 Variables, 86 Observations - UNF:6:IoKHUBaC8wFwyZ40ygz99g==
Deep kernel of genomic and near infrared predictions in multi-environment breeding trials Jul 18, 2019 Cuevas, Jaime; Montesinos-López, Osval A.; Juliana, Philomin; Pérez-Rodríguez, Paulino; Burgueño, Juan; Guzman, Carlos; Montesinos-López, Abelardo; Crossa, Jose, 2019, "Deep kernel of genomic and near infrared predictions in multi-environment breeding trials", https://hdl.handle.net/11529/10548180, CIMMYT Research Data & Software Repository Network, V4 In genomic prediction deep learning artificial neural network are part of machine learning methods that incorporate parametric, non-parametric and semi-parametric statistical models. Kernel methods are seeing more flexible, and easier to interpret than neural networks. Kernel met...
MAIZE2 DATA SET.rar Jul 18, 2019 - Deep kernel of genomic and near infrared predictions in multi-environment breeding trials RAR Archive - 5.6 MB - MD5: d6c9b7e9394f946bdec40c119a09f821
MAIZE3 DATA SET.rar Jul 18, 2019 - Deep kernel of genomic and near infrared predictions in multi-environment breeding trials RAR Archive - 13.4 MB - MD5: 293ec3553210607db6c9e18f87bfadc4
WHEAT1 DATA SET.rar Jul 18, 2019 - Deep kernel of genomic and near infrared predictions in multi-environment breeding trials RAR Archive - 280.2 KB - MD5: 4873ac1274ab0e4423c224ceee651c73
WHEAT4 DATA SET.rar Jul 18, 2019 - Deep kernel of genomic and near infrared predictions in multi-environment breeding trials RAR Archive - 6.0 MB - MD5: 629ebcd9f3f625eb8505e6764371e491
Replication Data for: Joint use of genome, pedigree and their interaction with environment for predicting the performance of wheat lines in new environments Jul 12, 2019 Howard, Reka; Gianola, Daniel; Montesinos-López, Osval A.; Juliana, Philomin; Singh, Ravi; Poland, Jesse; Shrestha, Sandesh; Pérez-Rodríguez, Paulino; Crossa, Jose; Jarquín, Diego, 2019, "Replication Data for: Joint use of genome, pedigree and their interaction with environment for predicting the performance of wheat lines in new environments", https://hdl.handle.net/11529/10548169, CIMMYT Research Data & Software Repository Network, V3 In this study, we evaluated genome-based prediction using 35,403 wheat lines from the Global Wheat Breeding Program of the International Maize and Wheat Improvement Center (CIMMYT). We implemented eight statistical models that included genome-wide molecular marker and pedigree in...

06CHTTEW-Grain Yield.xls

Aug 11, 2019 - International Early White Hybrid Trial - IEWH0611

MS Excel Spreadsheet - 29.0 KB -

Combined Analysis

06CHTTEW-Locations.xls

Aug 11, 2019 - International Early White Hybrid Trial - IEWH0611

MS Excel Spreadsheet - 28.5 KB -

Locations

Long-term tillage and residue management experiment in San Luis Potosí, Mexico

Aug 4, 2019

Verhulst, Nele; Fonteyne, Simon; Martinez Gamiño, Miguel-Angel; Saldivia Tejeda, Abel, 2019, "Long-term tillage and residue management experiment in San Luis Potosí, Mexico", https://hdl.handle.net/11529/10548248, CIMMYT Research Data & Software Repository Network, V1, UNF:6:IoKHUBaC8wFwyZ40ygz99g== [fileUNF]

An experiment initiated in 1996 in the highlands of the state of San Luis Potosí, Mexico, evaluated different tillage methods and levels of soil cover under permanent raised beds for their effects on yield, profitability, and soil quality in an irrigated, summer maize- winter oat...

DAT-PUB-Soledad.tab

Aug 4, 2019 - Long-term tillage and residue management experiment in San Luis Potosí, Mexico

Tabular Data - 3.8 KB - 5 Variables, 86 Observations -

Deep kernel of genomic and near infrared predictions in multi-environment breeding trials

Jul 18, 2019

Cuevas, Jaime; Montesinos-López, Osval A.; Juliana, Philomin; Pérez-Rodríguez, Paulino; Burgueño, Juan; Guzman, Carlos; Montesinos-López, Abelardo; Crossa, Jose, 2019, "Deep kernel of genomic and near infrared predictions in multi-environment breeding trials", https://hdl.handle.net/11529/10548180, CIMMYT Research Data & Software Repository Network, V4

In genomic prediction deep learning artificial neural network are part of machine learning methods that incorporate parametric, non-parametric and semi-parametric statistical models. Kernel methods are seeing more flexible, and easier to interpret than neural networks. Kernel met...

MAIZE2 DATA SET.rar

Jul 18, 2019 - Deep kernel of genomic and near infrared predictions in multi-environment breeding trials

RAR Archive - 5.6 MB -

MAIZE3 DATA SET.rar

Jul 18, 2019 - Deep kernel of genomic and near infrared predictions in multi-environment breeding trials

RAR Archive - 13.4 MB -

WHEAT1 DATA SET.rar

Jul 18, 2019 - Deep kernel of genomic and near infrared predictions in multi-environment breeding trials

RAR Archive - 280.2 KB -

WHEAT4 DATA SET.rar

Jul 18, 2019 - Deep kernel of genomic and near infrared predictions in multi-environment breeding trials

RAR Archive - 6.0 MB -

Replication Data for: Joint use of genome, pedigree and their interaction with environment for predicting the performance of wheat lines in new environments

Jul 12, 2019

Howard, Reka; Gianola, Daniel; Montesinos-López, Osval A.; Juliana, Philomin; Singh, Ravi; Poland, Jesse; Shrestha, Sandesh; Pérez-Rodríguez, Paulino; Crossa, Jose; Jarquín, Diego, 2019, "Replication Data for: Joint use of genome, pedigree and their interaction with environment for predicting the performance of wheat lines in new environments", https://hdl.handle.net/11529/10548169, CIMMYT Research Data & Software Repository Network, V3

In this study, we evaluated genome-based prediction using 35,403 wheat lines from the Global Wheat Breeding Program of the International Maize and Wheat Improvement Center (CIMMYT). We implemented eight statistical models that included genome-wide molecular marker and pedigree in...

Add Data

Share Dataverse

Link Dataverse

Reset Modifications